Image To Text

Image to Text

Extract all readable text from an image using OCR (Tesseract). Returns the full text content along with word-level bounding boxes and confidence scores.

When to Use

• Reading text content from a screenshot or design mockup
• Extracting UI copy (labels, buttons, headings) so you don't have to retype it
• Getting text positions and bounding boxes from a design image

How It Works

1. The image is passed to Tesseract.js for optical character recognition
2. Tesseract segments the image into lines and words
3. Returns the full text plus word-level details (position, confidence)

Usage

bash <skill-path>/scripts/image-to-text.sh <image-path> [language]

Arguments:

• image-path — Path to the image file (required)
• language — OCR language code (optional, defaults to eng). Common: eng, fra, deu, spa, chi_sim, jpn

Examples:

# Extract text from a screenshotbash <skill-path>/scripts/image-to-text.sh ./screenshot.png
# Extract French textbash <skill-path>/scripts/image-to-text.sh ./mockup.png fra

Output

{  "text": "Request work\nSuggestions\nPlumbing\nHVAC\nCleaning\nElectrical",  "confidence": 87.4,  "words": [    {      "text": "Request",      "confidence": 94.2,      "bbox": { "x0": 142, "y0": 180, "x1": 268, "y1": 204 }    },    {      "text": "work",      "confidence": 96.1,      "bbox": { "x0": 274, "y0": 180, "x1": 332, "y1": 204 }    }  ],  "lines": [    {      "text": "Request work",      "confidence": 95.1,      "bbox": { "x0": 142, "y0": 180, "x1": 332, "y1": 204 }    }  ]}

Present Results to User

After extracting text, present the content grouped by lines:

Extracted text (87.4% confidence):
  Request work  Suggestions  Plumbing  HVAC  Cleaning  Electrical
Found 6 lines, 6 words.

Use the extracted text directly when implementing UI copy from a design.

Troubleshooting

Low confidence / garbled text — Tesseract works best with clean, high-contrast text. Screenshots of rendered UI work well. Photos of text at angles or with noise may produce poor results.

Wrong language — Pass the correct language code as the second argument. Tesseract needs the right language model to recognize characters.

First run is slow — Tesseract downloads language data (~4MB for English) on the first run. Subsequent runs are faster.